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Traffic flow prediction : an intelligent scheme for forecasting traffic flow using air pollution data in smart cities with bagging ensemble
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Khan, Noor Ullah, Shah, Munam Ali, Maple, Carsten, Ahmed, Ejaz and Asghar, Nabeel (2022) Traffic flow prediction : an intelligent scheme for forecasting traffic flow using air pollution data in smart cities with bagging ensemble. Sustainability, 14 (7). e4164. doi:10.3390/su14074164 ISSN 2071-1050.
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WRAP-traffic-flow-prediction-intelligent-scheme-forecasting-traffic-flow-using-air-pollution-data-smart-cities-bagging-ensemble-Maple-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1290Kb) | Preview |
Official URL: https://doi.org/10.3390/su14074164
Abstract
Traffic flow prediction is the most critical part of any traffic management system in a smart city. It can help a driver to pick the most optimized way to their target destination. Air pollution data are often connected with traffic congestion and there exists plenty of research on the connection between air pollution and traffic congestion using different machine learning approaches. A scheme for efficiently predicting traffic flow using ensemble techniques such as bagging and air pollution has not yet been introduced. Therefore, there is a need for a more accurate traffic flow prediction system for the smart cities. The aim of this research is to forecast traffic flow using pollution data. The contribution is twofold: Firstly, a comparison has been made using different simple regression techniques to find out the best-performing model. Secondly, bagging and stacking ensemble techniques have been used to find out the most accurate model of the two comparisons. The results show that the K-Nearest Neighbors (KNN) bagging ensemble provides far better results than all the other regression models used in this study. The experimental results show that the KNN bagging ensemble model reduces the error rate in predicting the traffic congestion by more than 30%.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | T Technology > TE Highway engineering. Roads and pavements | ||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||
| SWORD Depositor: | Library Publications Router | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Traffic flow, Air -- Pollution -- Measurement, Machine learning, Climatic changes -- Environmental aspects, Internet of things, Time-series analysis -- Mathematical models | ||||||||||||
| Journal or Publication Title: | Sustainability | ||||||||||||
| Publisher: | MDPI | ||||||||||||
| ISSN: | 2071-1050 | ||||||||||||
| Official Date: | 31 March 2022 | ||||||||||||
| Dates: |
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| Volume: | 14 | ||||||||||||
| Number: | 7 | ||||||||||||
| Article Number: | e4164 | ||||||||||||
| DOI: | 10.3390/su14074164 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
| Date of first compliant deposit: | 4 May 2022 | ||||||||||||
| Date of first compliant Open Access: | 5 May 2022 | ||||||||||||
| RIOXX Funder/Project Grant: |
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